[1]牛 斌,吴 鹏,马 利,等.一种基于生成对抗网络的行为数据集扩展方法[J].计算机技术与发展,2019,29(07):43-48.[doi:10. 3969 / j. issn. 1673-629X. 2019. 07. 009]
　NIU Bin,WU Peng,MA Li,et al.A Behavior Data Set Extension Method Based on Generative Adversarial Network[J].,2019,29(07):43-48.[doi:10. 3969 / j. issn. 1673-629X. 2019. 07. 009]
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一种基于生成对抗网络的行为数据集扩展方法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年07期
页码:
43-48
栏目:
智能、算法、系统工程
出版日期:
2019-07-10

文章信息/Info

Title:
A Behavior Data Set Extension Method Based on Generative Adversarial Network
文章编号:
1673-629X(2019)07-0043-06
作者:
牛 斌; 吴 鹏; 马 利; 刘景巍
辽宁大学 信息学院,辽宁 沈阳 110036
Author(s):
NIU Bin; WU Peng; MA Li; LIU Jing-wei
School of Information,Liaoning University,Shenyang 110036,China
关键词:
数据生成; 深度学习; 循环神经网络; 生成式对抗网络
Keywords:
data generation; deep learning; recurrent neural networks; generative adversarial network
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 07. 009
摘要:
深度学习作为人工神经网络的分支,在图像识别领域有广泛的应用,但其数据集的不足导致模型学习不够完善。通过对深度学习的数据规模要求进行分析,针对人体行为识别中的应用,发现人体数据集的采集工作是一个极具耗时耗力的工程,很难满足目前深度学习网络的需求。 为了解决这一难题,提出了一种依靠原有的小规模数据集产生大量可靠数据集的半监督深度学习模型。 通过将循环神经网络和生成式对抗网络相结合的方法使循环神经网络学习到数据的序列关系和特征,使生成式对抗网络产生合理数据进而扩展人体行为数据集。 依靠该网络结构,可以很好地分析出采集数据的特征,并且依据这些特征可以生成大量的合理的数据,后经过数据处理等工作,形成可用于模型训练的可靠数据集,缓解了深度学习工作中数据集紧缺的问题。
Abstract:
As a branch of artificial neural network,deep learning has a wide range of applications in the field of image recognition. The lack of data sets leads to incomplete model learning. Through the analysis of the data size requirements of deep learning,it is found that the collection of human data sets is a very time-consuming and labor-intensive project for the application of human behavior recognition. It is difficult to meet the needs of the current deep learning network. To solve this problem,we propose a semi-supervised deep learning model that relies on the original small-scale data set to generate a large number of reliable data sets. By combining the cyclic neural network and the generative confrontation network,the cyclic neural network learns the sequence relationship and characteristics of the data,so that the generation-oriented network generates reasonable data and then expands the human behavior data set. Relying on this network structure,the characteristics of the collected data can be well analyzed,and a large amount of reasonable data can be generated according to these features,and then processed through data processing to form a reliable data set that can be used for model training, thereby alleviating the shortage of data sets in deep learning work.

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更新日期/Last Update: 2019-07-10